Pattern Lock Research Articles

Android Pattern Bypass using Microcontroller

Abstract: The goal is to exploit a potential security vulnerability in Android devices by using a microcontroller which goes by the name ATTiny85 connected via USB. The objective is to bypass the lock patterns or security measures typically in place on Android devices, which are designed to prevent unauthorized access. This could involve manipulating the USB connection to take control of the device's data or functions, potentially compromising the device's security. Such actions are unethical and illegal without proper authorization. It’s really important to acknowledge the amount of loss these attacks incur. The primary aim is to capitalize on a potential security vulnerability inherent in Android devices through the utility provided by microcontroller. This strategy is devised to circumvent the established lock patterns and security protocols usually implemented on Android devices, serving as a deterrent against unauthorized access. This approach may entail manipulating the USB connection to infiltrate the device's data or functionalities, posing a significant threat. It is crucial to emphasize that engaging in such activities is not only unethical but also illegal without explicit authorization. This underscores the paramount importance of upholding robust security measures on Android devices to proactively thwart any attempts at exploitation.

Wearable patterned single electrode-based triboelectric nanogenerator for peak-encoded interface

Advancement in human-machine interface (HMI) is indispensable for development of virtual/augmented reality, robotics control, and artificial intelligence (AI). However, there lack a simple facile electronics to achieve accurate finger motion recognition and remote robot control through simple signal processing. Herein, a wearable patterned single electrode-based triboelectric nanogenerator (PS-TENG) based on regenerated silk film (rSF) is developed for peak-encoded interface, which can accurately identify the handwritten numbers or letters. The patterned single electrode, composed of nine units with five different patterns, is successfully constructed by ink-jet printing Ag inks on the rSF. When handwriting on the PS-TENG, the generated output voltage with different peak amplitude can be transformed into a peak spectrum with spatial information. Handwriting numbers or letters can be accurately identified through simple digitization data processing of the peak-encoded spectrum without complex algorithms. Furthermore, the PS-TENG as peak-encoded interface enables precise game play, accurate pattern lock, and wireless robot control. Importantly, based on relative amplitude of output signals, the peak-encoded code is regardless of factors including absolute amplitude of output signals, sliding speed and environmental humidity. Consequently, the PS-TENG, characterized by self-powered feature, simple structure and straightforward signal processing, demonstrates remarkable identification accuracy of handwritten numbers and letters through decoding unique and stable peak spectrum, which would have great potential in the field of virtual control panels, HMI and AI.

Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks

Smartphones have become ubiquitous, allowing people to perform various tasks anytime and anywhere. As technology continues to advance, smartphones can now sense and connect to networks, providing context-awareness for different applications. Many individuals store sensitive data on their devices like financial credentials and personal information due to the convenience and accessibility. However, losing control of this data poses risks if the phone gets lost or stolen. While passwords, PINs, and pattern locks are common security methods, they can still be compromised through exploits like smudging residue from touching the screen. This research explored leveraging smartphone sensors to authenticate users based on behavioral patterns when operating the device. The proposed technique uses a deep learning model called DeepResNeXt, a type of deep residual network, to accurately identify smartphone owners through sensor data efficiently. Publicly available smartphone datasets were used to train the suggested model and other state-of-the-art networks to conduct user recognition. Multiple experiments validated the effectiveness of this framework, surpassing previous benchmark models in this area with a top F1-score of 98.96%.

Implicit IoT authentication using on-phone ANN models and breathing data

The expansionist nature of the Internet of Things (IoT) with smart sensing has integrated smart technology into the fabric of daily life. From digital health check-ups and personal property access to financial transactions are conveniently performed with the help of smart technologies, such as smart wearables. Therefore, the security of these devices and the entire IoT-connected cyber–physical space accessible through smart wearables has become a burning question. While the current knowledge-based security approaches (e.g., pattern locks, passwords, or PINs) cause a recall burden to the user, conventional one-time biometric-based methods are not adaptable to the tiny wearables. Therefore, this work presents an implicit IoT authentication utilizing breathing patterns to verify a user through continuous microphone sensing. The core piece of this work is the Breathing data-driven TensorFlow Lite framework-supported on-phone authentication application (i.e., BTL Auth app), which consists of an audio processing pipeline (to perform filtering and feature computation) and an artificial neural network model to verify the target user. From our detailed analysis of sequential and non-sequential neural networks trained with static and dynamic audio features, we can achieve an average accuracy of up to .93 when authenticating a user from breathing patterns. Finally, the BTL Auth app needs around 64 KB of memory and 5 s to verify the user.

Most complicated lock pattern-based seismological signal framework for automated earthquake detection

BackgroundSeismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. Material and methodWe developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selector-based feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. ResultsThe presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. ConclusionsOur model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.

WristAcoustic

PIN and pattern lock are difficult to accurately enter on small watch screens, and are vulnerable against guessing attacks. To address these problems, this paper proposes a novel implicit biometric scheme based on through-wrist acoustic responses. A cue signal is played on a surface transducer mounted on the dorsal wrist and the acoustic response recorded by a contact microphone on the volar wrist. We build classifiers using these recordings for each of three simple hand poses (relax, fist and open), and use an ensemble approach to make final authentication decisions. In an initial single session study (N=25), we achieve an Equal Error Rate (EER) of 0.01%, substantially outperforming prior on-wrist biometric solutions. A subsequent five recall-session study (N=20) shows reduced performance with 5.06% EER. We attribute this to increased variability in how participants perform hand poses over time. However, after retraining classifiers performance improved substantially, ultimately achieving 0.79% EER. We observed most variability with the relax pose. Consequently, we achieve the most reliable multi-session performance by combining the fist and open poses: 0.51% EER. Further studies elaborate on these basic results. A usability evaluation reveals users experience low workload as well as reporting high SUS scores and fluctuating levels of perceived exertion: moderate during initial enrollment dropping to slight during authentication. A final study examining performance in various poses and in the presence of noise demonstrates the system is robust to such disturbances and likely to work well in wide range of real-world contexts.

Vertical-Phase-Locking Effect in Efficient and Stable All-Polymer-Hosted Solar Cells

All-polymer solar cells with bulk-heterojunction films composed of polymeric donors and acceptors are distinguished for their outstanding device stability. However, unfavorable thermodynamic mixing of polymers induced severe phase-separated morphology related to electron–phonon coupling, limiting the practical development of high-efficiency organic solar cells (OSCs). Herein, we obtain an enhanced power conversion efficiency in all-polymer-hosted OSCs prepared by a guest–nanofill strategy, where a third component (small molecule guest, B1) is employed to manipulate the electron–phonon interactions associated with the diffusion-mediated excitons dissociation and charge transport properties of the binary film. Encouragingly, the guest-nanofilled OSCs exhibit notably enhanced thermal stability, as the nanofiller B1 molecules allow the vertical distribution of the donor: acceptor to exhibit a locked pattern during the aging process, which can be figuratively termed as “vertical-phase-locking effect”. The guest–nanofill strategy in this work provides a unique approach toward the development of high-performance all-polymer electronics.

Pattern lock screen detection method based on lightweight deep feature extraction

Pattern lock screen detection method based on lightweight deep feature extraction

SwipePass

Pattern lock-based authentication has been widely adopted in modern smartphones. However, this scheme relies essentially on passwords, making it vulnerable to various side-channel attacks such as the smudge attack and the shoulder-surfing attack. In this paper, we propose a second-factor authentication system named SwipePass, which authenticates a smartphone user by examining the distinct physiological and behavioral characteristics embedded in the user's pattern lock process. By emitting and receiving modulated audio using the built-in modules of the smartphone, SwipePass can sense the entire unlocking process and extract discriminative features to authenticate the user from the signal variations associated with hand dynamics. Moreover, to alleviate the burden of data collection in the user enrollment phase, we conduct an in-depth analysis of users' behaviors under different conditions and propose two augmentation techniques to significantly improve identification accuracy even when only a few training samples are available. Finally, we design a robust authentication model based on CNN-LSTM and One-Class SVM for user identification and spoofer detection. We implement SwipePass on three off-the-shelf smartphones and conduct extensive evaluations in different real-world scenarios. Experiments involving 36 participants show that SwipePass achieves an average identification accuracy of 96.8% while maintaining a false accept rate below 0.45% against various attacks.

Importance of Data Biometric in the Organisational Culture

Biometric data is a technology that users or workers use and saves a lot of time. This technology helps people to keep things, data, and information more safe. There are several types of biometric such as fingerprints scan, iris scan, palm scan, face scans and many more. Users no longer need to remember passwords or pin codes because of this technology. Users just have to scan their fingers or palm, face or eyes to unlock devices and this system is way more secure than any other systems. Though it is a high cost system, organisations can trust on this system because no one can easily hack this technology as every person has different characteristics. Passwords, pin codes, pattern locks can be easily hacked but hacking someone’s fingerprints or iris scan is quite impossible. Still hackers can hack this technology sometimes. Mainly voice commands get hacked easily because anyone can record a user's voice without consent. In that case, users have to be more careful about personal things and should not share single information with anyone

Pattern Lock and GPS-Based Motorcycle Security System

Pattern Lock and GPS-Based Motorcycle Security System

Intellectualized forensic technique for Android pattern locks

Intellectualized forensic technique for Android pattern locks

Smart City's Security Model for Management of Image Data on Cloud

In modern world of advancement, cloud technology is taking its grip. COVID-19 also enhance the use of the cloud services. The cloud concept comes with the new advancement of technology, but with that data breaches and data hacks are also increasing. In the cloud-based environment, we suggested the model of security enhancement which, make use pf role-based security model where the files are shared according to the role and the access of such file is controlled using TPA validation and apart for that the concept of authentication of user is also suggested using the Pattern Lock based graphical system for pattern formation. The pattern obtained are validated using the various of online password or pattern strength validation tools, results which are obtained proves that pattern obtained through the proposed concept performs better against the attack.

Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing.

Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.

Improved Haar Cascade Feature Extraction and Access Control Framework for Rich Internet Applications

In the digital era, internet application access protection against malicious access is a major concern. The security aspects such as password, pattern lock, and biometric are commonly preferred in mobile applications. To enforce the security feature in all aspects the face recognition padlock framework is used to prevent unauthorized or malicious accesses. The proposed system will secure the rich internet applications from malicious accesses and threats. The improved haar cascade classifier effectively detects and recognizes the objects. The model will take a 4*4 feature matrix to train the positive and negative images. The positive image is the factual confident image whereas the negative image is everything else other than the confident image. The system will take 100 good samples to train the model. The internet application is eligible to access only when the confidence rate is greater than 90 percent. The confident rate is the match probability of the trained image and actual image.

Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones

Pattern lock has been widely used in smartphones as a simple and effective authentication mechanism, which however is shown to be vulnerable to various attacks. In this paper, we design a novel authentication system for more secure pattern unlocking on smartphones. The basic idea is to utilize various behavior information of the user during pattern unlocking as additional authentication fingerprints, so that even if the pattern password is leaked to an attacker, the system remains safe and protected. To accommodate a variety of user contexts by our system, a context-aware module is proposed to distinguish any of such contexts (e.g., body postures when drawing the pattern) and use it to guide the authentication. Moreover, we design a polyline weighted strategy with overlapping based on the consistency of pattern lock, which analyzes the behavior information of the user during the unlock process in a fine-grained manner and takes an overall consideration the results of different polylines. Based on 14,850 samples collected from 77 participants, we have extensively evaluated the proposed system. The results demonstrate that it outperforms state-of-the-art implicit authentication based pattern lock approaches, and that each key module in our system is effective.

A new smart smudge attack using CNN

This paper deals with a well-known problem in the area of the smudge attacks: when a user draws a pattern to unlock the pattern lock on a smartphone screen, pattern extraction sometimes becomes difficult owing to the existence of the oily residuals around it. This is because the phone screen becomes obscured by these residuals, which significantly lower the guess rate of the pattern lock. To address this, this paper proposes a novel attack method based on a Convolutional Neural Network (CNN). CNNs are known to exhibit high accuracy in image classification. However, using only CNNs for the attack is not sufficient, because there are 389,112 possible patterns, and training the CNN for all the cases is difficult. We therefore propose two ideas to overcome the aforementioned problem. The first one is the application of ’Screen Segmentation,’ where we divide the screen into four segments to reduce the number of possible patterns to 1470 in each segment. The second is the use of pruning rules, which reduces the number of total pattern cases by combining the patterns in each segment. Furthermore, by applying the Android pattern lock constraints, we reduce the number of possible patterns. To validate the proposed idea, we collected 3500 image data by photographing the screen of Android smartphones and generated 367,500 image data based on their possible combinations. Using those data sets, we conducted an experiment whereby we investigated the success rate of our attack in various situations, dealing with different pattern lock lengths and type of prior application usage. The result shows that up to a pattern lock length of seven, the proposed method has on an average, 79% success rate, which is meaningful result in smudge attacks. In addition, in an ideal case where only the actual pattern lock is entered, without oily residuals, the proposed scheme supports an even higher performance, i.e., a 93% successful guess rate on an average.

Enabling Finger-Touch-Based Mobile User Authentication via Physical Vibrations on IoT Devices

This work enables mobile user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. The system we proposed extends finger-input authentication beyond touch screens to any solid surface for IoT devices (e.g., smart access systems and IoT appliances). Unlike passcode or biometrics-based solutions, it integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. The proposed system builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. New algorithms are developed to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics including contacting area, touching force, and etc. The system is implemented using a single pair of low-cost portable vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a stovetop or an appliance). Extensive experiments demonstrate that our system can authenticate users with high accuracy (e.g., more than 97 percent within two trials), low false positive rate (e.g., less 2 percent) and is robust to various types of attacks.

HIAuth: A Hierarchical Implicit Authentication System for IoT Wearables Using Multiple Biometrics

Day-by-day wearable devices, such as smartwatches, are getting popular with their wide range of services, including allowing financial transactions, unlocking cars, tracking health and fitness, among many others. Most often, these services are managed based on users’ personal data. Unfortunately, due to various limitations, most of the market-wearables either do not have any user authentication or have a knowledge-based authentication, such as passwords, PINs, or pattern locks, which are not only burdensome for users in the age of the internet of things (IoT) when the users are already flooded with so many passwords. Therefore, there is a need for a burden-free implicit authentication mechanism for wearables that can utilize different less-informative soft-biometric data easily obtainable from the market wearables. In this work, we present a hierarchical implicit authentication ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HIAuth</b> ) system that utilizes the heart rate, gait, and breathing audio signals based on their availability to authenticate a user. From our detailed analysis, we find that binary support vector machine (SVM) classifiers with radial basis function (RBF) kernels can achieve an average accuracy of 0.98 ± 0.04 (non-sedentary) and 0.94 ± 0.03 (sedentary), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score of 0.98 ± 0.03 (non-sedentary) and 0.94 ± 0.04 (sedentary), and genuine rejection rate of about 0.97 ± 0.07 (non-sedentary) and 0.93 ± 0.04 (sedentary), which shows the feasibility and promise of this work.

Effectiveness of Atm and Bank Security: Three Factor Authentications With Systemetic Review

Security is one of the key aspects in despite of time, location and domain. Providing a better security is an essential obligation to all sectors (state and private) and workers. Rendering better security is not only difficult but also fairly impossible due to growth of new technologies and constantly growing Information Technology domain. Rapid Improvement of banking sector provides many acceptable ways of managing the accounts, information and activities. This advancement has been a main driving force to the account activities with the help of Automatic Teller Machine (ATM). With the increase number of ATM usage among the public, hackers also use the technological growth to hack the data / information and apply the fraud actions. The said vulnerabilities have been increasing in a gradual manner and also ATM fraud has spread and become nightmare all around the world. This paper elaborates to mitigate the possible frauds can be happened at the ATM cubical with the hybrid multi-layered security concept. Here, previous studies were reviewed and compared in order to support to develop a system with the less loopholes. A three-layered architecture including PIN, OTP or finger print and pattern lock has been used in this system to mitigate the frauds. 400 students were selected to test the system and 384 students roughly 96% of students have successfully entered their pin number at the first attempt, in the second layer 95% of the students out of 384 students were succeeded and the third layer 99% of the students out of 363 students were success in drawing the patterns. The overall result of this evaluation process shows that, the suggested security tiers can be applicable since the accuracy in performance is better and accepted by the sample selected.